Distance in contextual network graph

1. A method for responding to a query to an enterprise data source based on relationships between objects in a meta-model semantic network, the method comprising: generating a contextual network graph comprising nodes and edges representing semantic objects and semantic relationships extracted from a meta-model of business objects from the meta-model semantic network; using the contextual network graph to generate a unique identifier for each node and associated edge; receiving a request to determine a cost and energy between a first and a second node within a predefined relationship level; using at least one processor, calculating a cost between two or more nodes in the contextual network graph prior to receiving the request; using the at least one processor, calculating an energy between the two or more nodes, wherein the energy describes how often a relationship between the two or more nodes is used; storing the cost, the energy, and the unique identifiers in a meta-model semantic network database; receiving a query indicating a first business object described by the meta-model of business objects and a relationship to the first business object; and using the meta-model semantic network to identify a second business object having the relationship to the first business object.

2. The method of claim 1 , wherein the cost identifies a distance between semantic objects located in the contextual network graph.

3. The method of claim 1 , wherein the meta-model of business objects includes a model that characterizes a conceptual meaning of elements of a definition of business objects, the model characterizing instances of enterprise data, the definition of business objects modeling an instance by defining attributes associated with the business object, the meta-model modeling the attributes and giving meaning to the attributes.

4. The method of claim 1 , wherein the semantic objects comprise business objects, documents, and business terminology.

5. The method of claim 1 , wherein the contextual network graph comprises a table having source object key data, target object key data, distance chain key data, level data, distance cost data, and distance energy data.

6. The method of claim 5 , wherein the level data identifies a length of a relation chain between nodes.

7. The method of claim 1 , further comprising: using the calculated cost and energy between the nodes of the contextual network graph to determine a frequency of use of a business object.

8. The method of claim 1 , further comprising: using the calculated cost and energy of the nodes and edges from the contextual network graph to determine a strength of a relationship between business objects.

9. The method of claim 1 , further comprising: compressing the unique identifiers; storing the compressed unique identifiers in a memory-based database; and using the compressed unique identifiers to perform the calculation.

10. An apparatus for responding to a query to an enterprise data source based on relationships between objects in a meta-model semantic network, the apparatus comprising: a meta-model semantic network stored in a memory, the meta-model semantic network comprising nodes and edges representing semantic objects and semantic relationships extracted from a meta-model of business objects from the meta-model semantic network; and a processor-based contextual network graph generator coupled to the meta-model semantic network, the contextual network graph generator configured to perform operations comprising: receiving a request to determine a cost and energy between a first and a second node within a predefined relationship level; calculating the cost between two or more nodes in the contextual network graph prior to receiving the request; calculating an energy between the two or more nodes in the contextual network prior to receiving the request, wherein the energy describes how often a relationship between the two or more nodes is used; using at least one hardware device, generating a unique identifier for each node and associated edge, storing the cost, the energy, and the unique identifier in a meta-model semantic network database; receiving a query indicating a first business object described by the meta-model of business objects and a relationship to the first business object; and using the meta-model semantic network to identify a second business object having the relationship to the first business object.

11. The apparatus of claim 10 , wherein the cost identifies a distance between semantic objects located in the contextual network graph.

12. The apparatus of claim 10 , wherein the energy identifies how often a relationship between the nodes is used, and wherein the semantic objects comprise business objects, documents, and business terminology.

13. The apparatus of claim 10 , wherein the processor-based contextual network graph comprises a table having source object key data, target object key data, distance chain key data, level data, distance cost data, and distance energy data, wherein the level data identifies a length of a relation chain between nodes.

14. The apparatus of claim 10 , wherein the processor-based contextual network graph generator is configured to use the calculated cost and energy between the nodes of the contextual network graph to determine a frequency of use of a business object.

15. The apparatus of claim 10 , wherein the processor-based contextual network graph generator is configured to use the calculated cost and energy of the nodes and edges from the contextual network graph to determine a strength of a relationship between business objects.

16. The apparatus of claim 10 , wherein the processor-based contextual network graph generator is further configured to: compress the unique identifiers; store the compressed unique identifiers in a memory-based database; and use the compressed unique identifiers to perform the calculation.

17. A non-transitory, computer-readable medium that stores instructions, which, when performed by a computer, cause the computer to perform operations comprising: generating a contextual network graph comprising nodes and edges representing semantic objects and semantic relationships extracted from a meta-model of business objects from a meta-model semantic network; using the contextual network graph to generate a unique identifier for each node and associated edge; receiving a request to determine a cost and energy between a first and a second node within a predefined relationship level; calculating the cost between two or more nodes in the contextual network graph prior to receiving the request; calculating an energy between the two or more nodes, wherein the energy describes how often a relationship between the two or more nodes is used; storing the cost, the energy, and the unique identifiers in a meta-model semantic network database; receiving a query indicating a first business object described by the meta-model of business objects and a relationship to the first business object; and using the meta-model semantic network to identify a second business object having the relationship to the first business object.

18. The computer-readable medium of claim 17 , wherein the cost identifies a distance between semantic objects located in the contextual network graph.

19. The computer-readable medium of claim 17 , wherein the meta-model of business objects includes a model that characterizes a conceptual meaning of elements of a definition of business objects, the model characterizing instances of enterprise data, the definition of business objects modeling an instance by defining attributes associated with the business object, the meta-model modeling the attributes and giving meaning to the attributes.

20. The computer-readable medium of claim 17 , wherein the semantic objects comprise business objects, documents, and business terminology.